1. bookVolume 3 (2007): Issue 2007 (January 2007)
Journal Details
License
Format
Journal
eISSN
2083-4608
ISSN
1895-8281
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
access type Open Access

The Exploitation of Wavelet De-Noising To Detect Bearing Faults

Published Online: 26 Feb 2008
Volume & Issue: Volume 3 (2007) - Issue 2007 (January 2007)
Page range: 7 - 16
Journal Details
License
Format
Journal
eISSN
2083-4608
ISSN
1895-8281
First Published
26 Feb 2008
Publication timeframe
4 times per year
Languages
English
The Exploitation of Wavelet De-Noising To Detect Bearing Faults

Failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, we proposed new approach for bearing fault detection based on the autocorrelation of wavelet de-noised vibration signal through a wavelet base function derived from the bearing impulse response. To improve the fault detection process the wavelet parameters (damping factor and center frequency) are optimized using maximization kurtosis criteria to produce wavelet base function with high similarity with the impulses generated by bearing defects, that leads to increase the magnitude of the wavelet coefficients related to the fault impulses and enhance the fault detection process. The results show the effectiveness of the proposed technique to reveal the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.

Keywords

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